Next Article in Journal
Introduction to the Physics of Ionic Conduction in Narrow Biological and Artificial Channels
Previous Article in Journal
Detection of Coronary Artery Disease Using Multi-Domain Feature Fusion of Multi-Channel Heart Sound Signals
Previous Article in Special Issue
A Transfer Learning Approach on the Optimization of Edge Detectors for Medical Images Using Particle Swarm Optimization
Article

AutoPPI: An Ensemble of Deep Autoencoders for Protein–Protein Interaction Prediction

Department of Computer Science, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Miquel Pons and Alessandro Giuliani
Entropy 2021, 23(6), 643; https://doi.org/10.3390/e23060643
Received: 15 April 2021 / Revised: 8 May 2021 / Accepted: 19 May 2021 / Published: 21 May 2021
(This article belongs to the Special Issue Computational Methods and Algorithms for Bioinformatics)
Proteins are essential molecules, that must correctly perform their roles for the good health of living organisms. The majority of proteins operate in complexes and the way they interact has pivotal influence on the proper functioning of such organisms. In this study we address the problem of protein–protein interaction and we propose and investigate a method based on the use of an ensemble of autoencoders. Our approach, entitled AutoPPI, adopts a strategy based on two autoencoders, one for each type of interactions (positive and negative) and we advance three types of neural network architectures for the autoencoders. Experiments were performed on several data sets comprising proteins from four different species. The results indicate good performances of our proposed model, with accuracy and AUC values of over 0.97 in all cases. The best performing model relies on a Siamese architecture in both the encoder and the decoder, which advantageously captures common features in protein pairs. Comparisons with other machine learning techniques applied for the same problem prove that AutoPPI outperforms most of its contenders, for the considered data sets. View Full-Text
Keywords: deep learning; autoencoders; protein–protein interaction deep learning; autoencoders; protein–protein interaction
Show Figures

Figure 1

MDPI and ACS Style

Czibula, G.; Albu, A.-I.; Bocicor, M.I.; Chira, C. AutoPPI: An Ensemble of Deep Autoencoders for Protein–Protein Interaction Prediction. Entropy 2021, 23, 643. https://doi.org/10.3390/e23060643

AMA Style

Czibula G, Albu A-I, Bocicor MI, Chira C. AutoPPI: An Ensemble of Deep Autoencoders for Protein–Protein Interaction Prediction. Entropy. 2021; 23(6):643. https://doi.org/10.3390/e23060643

Chicago/Turabian Style

Czibula, Gabriela, Alexandra-Ioana Albu, Maria I. Bocicor, and Camelia Chira. 2021. "AutoPPI: An Ensemble of Deep Autoencoders for Protein–Protein Interaction Prediction" Entropy 23, no. 6: 643. https://doi.org/10.3390/e23060643

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop